Pedestrian detection in infrared images using Aggregated Channel Features

Brehar, Raluca; Vancea, Cristian; Nedevschi, Sergiu

doi:10.1109/iccp.2014.6936964

Cited by 15 publications

(17 citation statements)

References 21 publications

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“…We have extended the dataset employed in [30] that contains annotations of fully visible pedestrians, occluded pedestrians, groups of pedestrians, and bicyclists. Part of the sequences have been used for training the classifiers and the others have been used for test.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

Brehar

Nedevschi

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible. A popular and successful approach for monocular intensity pedestrian detection is based on the approximation (instead of computation) of image features for multiple scales based on the features computed on set of predefined scales. We port this idea to the infrared domain. Our contributions reside in the combination of four channel features, namely infrared, histogram of gradient orientations, normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared sensors. Multiple scale feature computation is done by feature approximation. Another contribution is the study of different formulations for Local Binary Patterns like uniform patterns and rotation invariant patterns and their effect on detection performance. The detection speed is also boosted by the aid of a fast morphological based region of interest generator. We vary the number of approximated scales per octave and study the impact on execution time and accuracy. A reasonable result hits a speed of 18fps with a log average miss rate of 39%.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…1. Main modules for online testing refines the work in [30] by reducing the region of interest generator and by analyzing the effect of several versions of Local Binary Pattern features on the detection accuracy. We also integrate the LBP features in the Aggregated Channel Feature framework [2].…”

Section: Methods Descriptionmentioning

confidence: 99%

Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

Brehar

Nedevschi

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…Channel Features refer to a collection of spatially discriminative features by linear or non-linear transformations of the input image. Over the past decades, channel features extraction techniques have been received an increasing interest with successful applications in pedestrian detection [28], [30] and face detection [31]- [33]. Owing to their high representation ability, a variety of channel features have been widely used in geospatial object detection.…”

Section: A Channel Featuresmentioning

confidence: 99%

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

Hong

Tian

et al. 2019

IEEE Trans. Geosci. Remote Sensing

204

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This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Geoscience andRemote Sensing on IEEE Xplore. With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-frequency channel feature (SFCF) by jointly considering the rotation-invariant channel features constructed in frequency domain and the original spatial channel features (e.g., color channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous stateof-the-art methods.

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“…It is more convenient and faster to use simpler and still very efficient descriptors such as the features of the HOG detector or the ICF-based detectors. In their work, Brehar et al proposed to combine a search space reduction technique with a modified ACF detector to reach near real-time detection [9].…”

Section: Human Detection In the Infrared Spectrummentioning

confidence: 99%

Collaborative training of far infrared and visible models for human detection

Blondel

Potelle

Pégard

et al. 2019

Int. J. Simul. Multidisci. Des. Optim.

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This paper is about the collaborative training of a far infrared and a visible spectrum human detector; the idea is to use the strengths of one detector to fill the weaknesses of the other detector and vice versa. At first infrared and visible human detectors are pre-trained using initial training datasets. Then, the detectors are used to collect as many detections as possible. The validity of each detection is tested using a low-level criteria based on an objectness measure. New training data are generated in a coupled way based on these detections and thus reinforce both the infrared and the visible human detectors in the same time. In this paper, we showed that this semi-supervised approach can significantly improve the performance of the detectors. This approach is a good solution to generate infrared training data, this kind of data being rarely available in the community.

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Pedestrian detection in infrared images using Aggregated Channel Features

Cited by 15 publications

References 21 publications

Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

Pedestrian detection in infrared images using HOG, LBP, gradient magnitude and intensity feature channels

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

Collaborative training of far infrared and visible models for human detection

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